TY - JOUR
T1 - Perspectives of Patients With Chronic Diseases on Future Acceptance of AI–Based Home Care Systems
T2 - Cross-Sectional Web-Based Survey Study
AU - Wang, Bijun
AU - Asan, Onur
AU - Mansouri, Mo
N1 - Publisher Copyright:
©Bijun Wang, Onur Asan, Mo Mansouri.
PY - 2023
Y1 - 2023
N2 - Background: Artificial intelligence (AI)–based home care systems and devices are being gradually integrated into health care delivery to benefit patients with chronic diseases. However, existing research mainly focuses on the technical and clinical aspects of AI application, with an insufficient investigation of patients’ motivation and intention to adopt such systems. Objective: This study aimed to examine the factors that affect the motivation of patients with chronic diseases to adopt AI-based home care systems and provide empirical evidence for the proposed research hypotheses. Methods: We conducted a cross-sectional web-based survey with 222 patients with chronic diseases based on a hypothetical scenario. Results: The results indicated that patients have an overall positive perception of AI-based home care systems. Their attitudes toward the technology, perceived usefulness, and comfortability were found to be significant factors encouraging adoption, with a clear understanding of accountability being a particularly influential factor in shaping patients’ attitudes toward their motivation to use these systems. However, privacy concerns persist as an indirect factor, affecting the perceived usefulness and comfortability, hence influencing patients’ attitudes. Conclusions: This study is one of the first to examine the motivation of patients with chronic diseases to adopt AI-based home care systems, offering practical insights for policy makers, care or technology providers, and patients. This understanding can facilitate effective policy formulation, product design, and informed patient decision-making, potentially improving the overall health status of patients with chronic diseases.
AB - Background: Artificial intelligence (AI)–based home care systems and devices are being gradually integrated into health care delivery to benefit patients with chronic diseases. However, existing research mainly focuses on the technical and clinical aspects of AI application, with an insufficient investigation of patients’ motivation and intention to adopt such systems. Objective: This study aimed to examine the factors that affect the motivation of patients with chronic diseases to adopt AI-based home care systems and provide empirical evidence for the proposed research hypotheses. Methods: We conducted a cross-sectional web-based survey with 222 patients with chronic diseases based on a hypothetical scenario. Results: The results indicated that patients have an overall positive perception of AI-based home care systems. Their attitudes toward the technology, perceived usefulness, and comfortability were found to be significant factors encouraging adoption, with a clear understanding of accountability being a particularly influential factor in shaping patients’ attitudes toward their motivation to use these systems. However, privacy concerns persist as an indirect factor, affecting the perceived usefulness and comfortability, hence influencing patients’ attitudes. Conclusions: This study is one of the first to examine the motivation of patients with chronic diseases to adopt AI-based home care systems, offering practical insights for policy makers, care or technology providers, and patients. This understanding can facilitate effective policy formulation, product design, and informed patient decision-making, potentially improving the overall health status of patients with chronic diseases.
KW - AI
KW - adoption
KW - artificial intelligence
KW - attitude
KW - attitudes
KW - chronic
KW - consumer informatics
KW - cross-sectional
KW - home care
KW - intent
KW - intention
KW - motivation
KW - perception
KW - perceptions
KW - technology acceptance model
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U2 - 10.2196/49788
DO - 10.2196/49788
M3 - Article
AN - SCOPUS:85177838523
VL - 10
JO - JMIR Human Factors
JF - JMIR Human Factors
IS - 1
M1 - e49788
ER -